Abstract:

Aiming at the problems of traditional neural network in mining transformer time series relationship, poor generalization of classification and low accuracy of classification for heterogeneous data, this paper proposes an improved transformer fault diagnosis model based on bidirectional recurrent neural network. The model extracts features through bidirectional recurrent neural network, fuses the features at the front and back time, classifies the feature data using multi?kernel learning support vector machine method, and fuses the features in multi?kernel learning support vector machine, so as to improve the accuracy of feature data classification. The accuracy and validity of the bidirectional recurrent neural network based on multi?kernel learning support vector machine model are tested through numerical simulation analysis of the temporal channel length for sequential network diagnostic performance, the influence of and multi?kernel learning on the generalization ability of support vector machines, and the influence on heterogeneous data processing capabilities. The experimental results show that the diagnosis performance of the bidirectional recurrent neural network based on multi?kernel learning support vector machine is better, and the prediction accuracy of the model is higher than that of several commonly used neural networks.

Key words: Transformer fault diagnosis, Bidirectional recurrent neural network, Multi?kernel learning, Support vector machine, Kernel fusion, Long short?term memory

摘要：

针对传统神经网络对变压器时序关系挖掘缺失、分类泛化性差、对异构数据分类准确率低的问题，提出了一种基于改进的双向循环神经网络的变压器故障诊断模型。该模型通过双向循环神经网络进行特征提取，将前后时刻的特征进行融合，采用多核学习支持向量机方法对特征数据进行分类，在多核学习支持向量机中进行核融合，从而提高特征数据分类的准确性。数值仿真分析了时序通道对长短时序网络诊断性能的影响，以及多核学习对支持向量机泛化能力和对异构数据处理能力的影响，通过变压器故障数据分类试验验证了基于多核学习支持向量机的双向循环神经网络模型的正确性和有效性。结果表明，基于多核学习支持向量机的双向循环网络诊断性能较好，与几种常用的神经网络相比，模型预测正确率更高。

关键词: 变压器故障诊断, 双向循环神经网络, 多核学习, 支持向量机, 核融合, 长短期记忆网络